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--- |
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language: rna |
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tags: |
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- Biology |
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- RNA |
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license: |
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- agpl-3.0 |
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size_categories: |
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- 1K<n<10K |
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source_datasets: |
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- multimolecule/rfam |
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task_categories: |
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- text-generation |
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- fill-mask |
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task_ids: |
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- language-modeling |
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- masked-language-modeling |
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pretty_name: bpRNA-new |
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library_name: multimolecule |
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--- |
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# bpRNA-new |
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bpRNA-new is a database of single molecule secondary structures annotated using bpRNA. |
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bpRNA-new is a dataset of RNA families from Rfam 14.2, designed for cross-family validation to assess generalization capability. |
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It focuses on families distinct from those in [bpRNA-1m](../bprna), providing a robust benchmark for evaluating model performance on unseen RNA families. |
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## Disclaimer |
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This is an UNOFFICIAL release of the bpRNA-new by Kengo Sato, et al. |
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**The team releasing bpRNA-new did not write this dataset card for this dataset so this dataset card has been written by the MultiMolecule team.** |
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## Dataset Description |
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- **Homepage**: https://multimolecule.danling.org/datasets/bprna-new |
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- **datasets**: https://huggingface.co/datasets/multimolecule/bprna-new |
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- **Point of Contact**: [Kengo Sato](mailto:satoken@bio.keio.ac.jp) |
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## Related Datasets |
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- [bpRNA-1m](https://huggingface.co/datasets/multimolecule/bprna): A database of single molecule secondary structures annotated using bpRNA. |
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- [bpRNA-spot](https://huggingface.co/datasets/multimolecule/bprna-spot): A subset of bpRNA-1m that applies [CD-HIT (CD-HIT-EST)](https://sites.google.com/view/cd-hit) to remove sequences with more than 80% sequence similarity from bpRNA-1m. |
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- [ArchiveII](https://huggingface.co/datasets/multimolecule/archiveii): A database of RNA secondary with the same families as RNAStrAlign, usually used for testing. |
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## License |
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This dataset is licensed under the [AGPL-3.0 License](https://www.gnu.org/licenses/agpl-3.0.html). |
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```spdx |
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SPDX-License-Identifier: AGPL-3.0-or-later |
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``` |
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## Citation |
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```bibtex |
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@article{sato2021rna, |
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author = {Sato, Kengo and Akiyama, Manato and Sakakibara, Yasubumi}, |
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journal = {Nature Communications}, |
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month = feb, |
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number = 1, |
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pages = {941}, |
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publisher = {Springer Science and Business Media LLC}, |
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title = {{RNA} secondary structure prediction using deep learning with thermodynamic integration}, |
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volume = 12, |
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year = 2021 |
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} |
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``` |
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